{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:47:34Z","timestamp":1778258854898,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T00:00:00Z","timestamp":1614297600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010607","name":"Universit\u00e0 degli Studi di Perugia","doi-asserted-by":"publisher","award":["RICBA17MR"],"award-info":[{"award-number":["RICBA17MR"]}],"id":[{"id":"10.13039\/501100010607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010607","name":"Universit\u00e0 degli Studi di Perugia","doi-asserted-by":"publisher","award":["RICBA18MR"],"award-info":[{"award-number":["RICBA18MR"]}],"id":[{"id":"10.13039\/501100010607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.<\/jats:p>","DOI":"10.3390\/s21051645","type":"journal-article","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T21:02:34Z","timestamp":1614373354000},"page":"1645","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4908-9769","authenticated-orcid":false,"given":"Nicholas","family":"Cartocci","sequence":"first","affiliation":[{"name":"Department of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marcello R.","family":"Napolitano","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8417-9372","authenticated-orcid":false,"given":"Gabriele","family":"Costante","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3104-8782","authenticated-orcid":false,"given":"Mario L.","family":"Fravolini","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Perugia, Via G. Duranti, 67, 06125 Perugia, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,26]]},"reference":[{"key":"ref_1","unstructured":"Ding, S. (2008). Model-Based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools, Springer."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/S1474-6670(17)51119-2","article-title":"Analytical Redundancy Methods in Fault Detection and Isolation-Survey and Synthesis","volume":"24","author":"Gertler","year":"1991","journal-title":"IFAC Proc. Vol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Simani, S., Fantuzzi, C., and Patton, R. (2003). Model-Based Fault Diagnosis in Dynamic Systems Using Identification Techniques, Springer.","DOI":"10.1007\/978-1-4471-3829-7"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/0005-1098(84)90098-0","article-title":"Process fault detection based on modeling and estimation methods\u2014A survey","volume":"20","author":"Isermann","year":"1984","journal-title":"Automatica"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/0005-1098(88)90073-8","article-title":"Detecting changes in signals and systems\u2014A survey","volume":"24","author":"Basseville","year":"1988","journal-title":"Automatica"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gertler, J. (2017). Fault Detection and Diagnosis in Engineering Systems, Routledge.","DOI":"10.1201\/9780203756126"},{"key":"ref_7","unstructured":"Patton, R.J., Frank, P.M., and Clark, R.N. (2013). Issues of Fault Diagnosis for Dynamic Systems, Springer Science & Business Media."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques\u2014Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Blanke, M., Kinnaert, M., Lunze, J., and Staroswiecki, M. (2016). Diagnosis and Fault Tolerant Control, Springer.","DOI":"10.1007\/978-3-662-47943-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.ifacol.2018.09.618","article-title":"Optimal fault detection and estimation: A unified scheme and least squares solutions","volume":"51","author":"Ding","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_11","unstructured":"Li, L., Ding, S., and Peng, X. (2020). Optimal Observer-based Fault Detection and Estimation Approaches for T-S Fuzzy Systems. IEEE Trans. Fuzzy Syst."},{"key":"ref_12","unstructured":"Martinez-Guerra, R., and Mata-Machuca, J.L. (2016). Fault Detection and Diagnosis in Nonlinear Systems, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sobhani-Tehrani, E., and Khorasani, K. (2009). Fault Diagnosis of Nonlinear Systems Using a Hybrid Approach, Springer Science & Business Media.","DOI":"10.1007\/978-0-387-92907-1"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1002\/rnc.3141","article-title":"Distributed sensor fault detection and isolation for multimachine power systems","volume":"24","author":"Zhang","year":"2014","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2004.12.002","article-title":"Model-based fault-detection and diagnosis\u2014Status and applications","volume":"29","author":"Isermann","year":"2005","journal-title":"Annu. Rev. Control"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ding, S. (2014). Data-Driven Design of Fault Diagnosis and Fault-Tolerant Control Systems, Springer.","DOI":"10.1007\/978-1-4471-6410-4"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1016\/j.jprocont.2012.06.009","article-title":"A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process","volume":"22","author":"Yin","year":"2012","journal-title":"J. Process Control"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Mrugalski, M. (2014). Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis, Springer.","DOI":"10.1007\/978-3-319-01547-7"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bocaniala, C.D., and Palade, V. (2006). Computational intelligence methodologies in fault diagnosis: Review and state of the art. Computational Intelligence in Fault Diagnosis, Springer.","DOI":"10.1007\/978-1-84628-631-5_1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106587","DOI":"10.1016\/j.ymssp.2019.106587","article-title":"Applications of machine learning to machine fault diagnosis: A review and roadmap","volume":"138","author":"Lei","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Estrada, F.R., M\u00e9ndez L\u00f3pez, L., Santos-Ruiz, I., and Valencia-Palomo, G. (2021). Detecci\u00f3n de fallas en veh\u00edculos a\u00e9reos no tripulados mediante se\u00f1ales de orientaci\u00f3n y t\u00e9cnicas de aprendizaje de m\u00e1quina. Revista Iberoamericana de Autom\u00e1tica e Inform\u00e1tica Industrial RIAI.","DOI":"10.4995\/riai.2020.14031"},{"key":"ref_22","unstructured":"Schaefer, R. (2003). Unmanned Aerial Vehicle Reliability Study, Office of the Secretary of Defense."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1016\/j.conengprac.2010.12.009","article-title":"AIRBUS state of the art and practices on FDI and FTC in flight control system","volume":"19","author":"Goupil","year":"2011","journal-title":"Control Eng. Pract."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/0376-0421(96)82785-0","article-title":"A review of fault management techniques used in safety-critical avionic systems","volume":"32","author":"Johnson","year":"1996","journal-title":"Prog. Aerosp. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1177\/0954410011421717","article-title":"Model-based fault diagnosis for aerospace systems: A survey","volume":"226","author":"Marzat","year":"2012","journal-title":"Proc. Inst. Mech. Eng. Part G J. Aerosp. Eng."},{"key":"ref_26","unstructured":"Farsoni, S., and Simani, S. (2016). Robust Fault Diagnosis and Fault Tolerant Control of Wind Turbines: Data-Driven and Model-Based Approaches, Scholars\u2019 Press."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chu, E., Gorinevsky, D., and Boyd, S. (2010). Detecting Aircraft Performance Anomalies from Cruise Flight Data, AIAA Infotech@Aerospace.","DOI":"10.2514\/6.2010-3307"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, L., Gariel, M., Hansman, R.J., and Palacios, R. (2011, January 16\u201320). Anomaly detection in onboard-recorded flight data using cluster analysis. Proceedings of the 2011 IEEE\/AIAA 30th Digital Avionics Systems Conference, Seattle, WA, USA.","DOI":"10.1109\/DASC.2011.6096223"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dani, M.C., Freixo, C., Jollois, F., and Nadif, M. (2015, January 7\u201314). Unsupervised anomaly detection for Aircraft Condition Monitoring System. Proceedings of the 2015 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2015.7119138"},{"key":"ref_30","first-page":"587","article-title":"Analysis of Flight Data Using Clustering Techniques for Detecting Abnormal Operations","volume":"12","author":"Li","year":"2015","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.conengprac.2014.12.007","article-title":"Double-model adaptive fault detection and diagnosis applied to real flight data","volume":"36","author":"Lu","year":"2015","journal-title":"Control Eng. Pract."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.conengprac.2018.07.002","article-title":"Experimental interval models for the robust Fault Detection of Aircraft Air Data Sensors","volume":"78","author":"Fravolini","year":"2018","journal-title":"Control Eng. Pract."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/TCST.2017.2758345","article-title":"Data-Driven Schemes for Robust Fault Detection of Air Data System Sensors","volume":"27","author":"Fravolini","year":"2019","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_34","unstructured":"(2020, December 22). Tecnam P92 Webpage. Available online: https:\/\/www.tecnam.com\/aircraft\/p92-echo-mkii\/."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1080\/00207179508921908","article-title":"Optimal residual decoupling for robust fault diagnosis","volume":"61","author":"Gertler","year":"1995","journal-title":"Int. J. Control"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1016\/S0005-1098(97)00004-6","article-title":"Information criteria for residual generation and fault detection and isolation","volume":"33","author":"Basseville","year":"1997","journal-title":"Automatica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3182\/20020721-6-ES-1901.00753","article-title":"Design of Directional Residuals for Optimal Testability","volume":"35","author":"Hu","year":"2002","journal-title":"IFAC Proc. Vol."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1016\/j.automatica.2009.02.027","article-title":"Reconstruction-based contribution for process monitoring","volume":"45","author":"Alcala","year":"2009","journal-title":"Automatica"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.automatica.2005.10.009","article-title":"Robust residual generation for diagnosis including a reference model for residual behavior","volume":"42","author":"Frisk","year":"2006","journal-title":"Automatica"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.conengprac.2013.10.002","article-title":"Robust fault detection for Uncertain Unknown Inputs LPV system","volume":"22","author":"Varrier","year":"2014","journal-title":"Control Eng. Pract."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1002\/rnc.3365","article-title":"An LMI approach to robust fault estimation for a class of nonlinear systems","volume":"26","author":"Witczak","year":"2016","journal-title":"Int. J. Robust Nonlinear Control"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1072056","DOI":"10.1155\/2018\/1072056","article-title":"Air Data Sensor Fault Detection with an Augmented Floating Limiter","volume":"2018","author":"Balzano","year":"2018","journal-title":"Int. J. Aerosp. Eng."},{"key":"ref_43","unstructured":"Leondes, C.T. (1996). Techniques in Discrete and Continuous Robust Systems, Academic Press."},{"key":"ref_44","first-page":"49","article-title":"On the generalized distance in statistics","volume":"2","author":"Mahalanobis","year":"1936","journal-title":"Proc. Natl. Inst. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","article-title":"The Mahalanobis distance","volume":"50","author":"Massart","year":"2000","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.arcontrol.2012.09.004","article-title":"Survey on data-driven industrial process monitoring and diagnosis","volume":"36","author":"Qin","year":"2012","journal-title":"Annu. Rev. Control"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Cartocci, N., Costante, G., Napolitano, M.R., Valigi, P., Crocetti, F., and Fravolini, M.L. (2020, January 16\u201319). PCA Methods and Evidence Based Filtering for Robust Aircraft Sensor Fault Diagnosis. Proceedings of the 2020 28th Mediterranean Conference on Control and Automation (MED), Saint-Rapha\u00ebl, France.","DOI":"10.1109\/MED48518.2020.9182973"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"478373","DOI":"10.1155\/2012\/478373","article-title":"Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes","volume":"2012","author":"Yang","year":"2012","journal-title":"J. Control Sci. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1109\/TAC.1986.1104419","article-title":"A geometric approach to the synthesis of failure detection filters","volume":"31","author":"Massoumnia","year":"1986","journal-title":"IEEE Trans. Autom. Control"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S1474-6670(17)36501-1","article-title":"Design of optimal directional residuals for linear dynamic systems","volume":"36","author":"Hu","year":"2003","journal-title":"IFAC Proc. Vol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/0005-1098(86)90031-2","article-title":"Optimally robust redundancy relations for failure detection in uncertain systems","volume":"22","author":"Lou","year":"1986","journal-title":"Automatica"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"S\u00e4rkk\u00e4, S. (2013). Bayesian Filtering and Smoothing, Cambridge University Press.","DOI":"10.1017\/CBO9781139344203"},{"key":"ref_53","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"676","DOI":"10.1016\/j.jprocont.2010.03.003","article-title":"Nonlinear process monitoring based on linear subspace and Bayesian inference","volume":"20","author":"Ge","year":"2010","journal-title":"J. Process Control"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"7723","DOI":"10.1109\/TIE.2016.2591902","article-title":"Normalized Relative RBC-Based Minimum Risk Bayesian Decision Approach for Fault Diagnosis of Industrial Process","volume":"63","author":"Zheng","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1877","DOI":"10.1109\/TII.2017.2658732","article-title":"Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes with Big Data","volume":"13","author":"Zhu","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhou, W., Yang, W., Wang, Y., and Zhang, H. (2018, January 25\u201327). Generalized Reconstruction-Based Contribution for Multiple Faults Diagnosis with Bayesian Decision. Proceedings of the 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS), Enshi, China.","DOI":"10.1109\/DDCLS.2018.8516010"},{"key":"ref_58","unstructured":"Basseville, M., and Nikiforov, I.V. (1993). Detection of Abrupt Changes\u2014Theory and Application, Prentice Hall, Inc."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly Detection: A Survey. ACM Comput. Surv., 41.","DOI":"10.1145\/1541880.1541882"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Draper, N., and Smith, H. (1998). Applied Regression Analysis, Wiley. [3rd ed.]. A Wiley-Interscience Publication.","DOI":"10.1002\/9781118625590"},{"key":"ref_61","unstructured":"The Mathworks Inc. (2020). MATLAB\u2014MathWorks, The Mathworks Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1645\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:29:37Z","timestamp":1760160577000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/5\/1645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,26]]},"references-count":61,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21051645"],"URL":"https:\/\/doi.org\/10.3390\/s21051645","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,26]]}}}